Artificial Neural Networks for Robot Control: The New Frontier

Robotic control days are over with artificial neural networks (ANNs).The brain-inspired intelligent tools allow robots to learn, adapt, and make decisions based on different circumstances.This article will present the interesting subject of ANNs for robot control and usage, advantages, and disadvantages.

What are artificial neural networks?

Imagine a group of “neurones” by themselves, conversing with each other. That is as good a definition of an artificial neural network as there is. ANNs are employed for pattern recognition, learning, and prediction based on their own pasts, mirroring the functioning of our brain. ANNs are the workhorses of machine learning and are revolutionising applications from face recognition to robots.

How ANNs Work with Robots

ANNs are the “mind” of robots. They get information from sensors like touch sensors and cameras and decide based on them what has to be done and where to direct the robot. In normal programming, every action has to be explicitly defined, but ANNs enable robots to learn by doing.

Learning From Examples: Supervised Learning

Supervised learning is the most common one. In this case, the ANN is being presented with some examples. One of them is chosen. For instance, if we wanted to teach a robot how to pick up objects of different types, we would present it with some examples of all the objects and how each of the objects must be picked up. The ANN is trained in such a manner that the ANN learns the visual shape of the object and projects it onto the action of grasping the object.

Learning by Trial and Error: Reinforcement Learning

Reinforcement learning is also a very handy method. The robot learns by trial and error. It gets rewarded for a good deed and punished for a bad deed. Gradually, the ANN learns so much that it receives maximum rewards and performs the task in the best way possible. For example, the robot is learning to walk—stumbles at first but becomes a walking expert without any hassle.

Applications of ANNs to robot control

Applications of artificial neural networks to robot control are many and expand amazingly fast.

Manufacturing and Automation

ANNs enable robots to build with sophisticated complexity at high levels of precision accuracy in factories. Robots can be trained to adjust production line variations even as the speed of eliminating motion boosts production. It is easier to make and is cost-effective.

Healthcare

ANN-based robot-assisted surgery can carry out minimally invasive surgery with greater precision than physicians. They can even accelerate healing by learning to adjust to the specific patient’s needs.

Exploration and Navigation

Robots that have to go to hostile areas, i.e., space or the ocean floor, can employ ANNs so that they are able to cross hostile terrain and decide on their own. Robots can be programmed to navigate through obstructions and reach the destination in the shortest path possible. This is required for that application, which requires little or no human input.

Service Robots

From sweeping a floor to hand-delivering a product, there is no equal to service robots. ANNs allow the robot family to perceive the world, communicate with people, and do anything without human control.

Advantages of Using ANNs in Robotics

The advantages of using artificial neural networks in robotics control are:

• Flexibility: The robots learn and adjust to new conditions without reprogramming.
• Autonomy: The machines do the work themselves with minimal human intervention.
• Enhanced Performance: ANNs have the capability of bringing in increased accuracy, speed, and efficiency of robotic action.
• Conquering Complexity: Robots are capable of executing complex actions that would be too difficult or even impossible otherwise to program by hand.

Issues and Trends

ANNs have potential, but the following points have to be worked out:

• Data Needs: ANNs need quality data in massive quantities to train.
• Explainability: ANNs cannot explain how they come to a decision, and debugging and optimisation are expensive because of this.
• Real-time Performance: Real-time data computation is computationally expensive, especially for complicated processes.

These issues are currently being researched by researchers. Some of the things that must be done in the future are designing more sophisticated learning algorithms, explaining ANNs, and hardware design with a bit of specificity so that neural networks can be derived.

Examples of ANNs Being Used

• Autonomous cars: Cars utilise ANNs to sense the world, determine actions, and travel on highways.
• Amazon warehouse robots: robots utilise ANNs to select orders quickly, ship orders, route planning, and collision avoidance.
• Robotic surgeons: Robots use ANNs to map out surgeons as a means of making complex surgeries more accurate.

Future of Robotics ANNs Control

Artificial neural networks will transform robotics control. Science and technology can provide smarter, more intelligent robots within a few years. Industry and healthcare, space and service industries—ANNs are making robots intelligent, responsive, and transforming the world. The world has no boundaries.

Artificial Neural Networks: A Game Changer

ANNs are not a machine but a paradigm when we discuss automation and intelligence.

As the robots continue learning and growing, ANNs are taking the whole new world with them to dominate, paving the way for the future with robots being integrated.

The future is being rolled out as we speak, and hope is overflowing in the whole world.

The Impact on Industries

The contribution of artificial neural networks to robot control has far-reaching implications, even outside the arena of robot control:

• Manufacturing: More automation on a mass scale, quality improvement, and maximum efficiency.
• Logistics: effective warehouse handling, quicker delivery, and cost reduction.
• Healthcare: enhanced surgery, an individualised rehabilitation program, and enhanced patient care.
• Agriculture: autonomous reaping in fields, precision agriculture, and best utilisation of resources.

Moral Issues

With each and every technological revolution, there will be some ethics to follow. ANNs need to be utilised, developed, and applied on ethical grounds. This encompasses strangulating fear of losing jobs and sending robots into space for human upgrade.

Learning More About ANNs and Robotics

If you want to know more about artificial neural networks and control of robots, you can simply search for many sources where you can learn more:

• Online tutorials and courses
• Publications and books
• Scientific papers and conferences

Reading what is currently going on about this intriguing subject, you step into the world of robots and artificial intelligence.

Being Master of the Future: Conclusion

Artificial neural networks are revolutionising robotic control to the point that the robots learn and evolve such that presently it is easy for the robots to perform tasks of a complicated nature up to the extent of autonomy and precision never seen before.

The issue is out there, but the promise is interesting. Let us embrace this technology and put some ethics into it, and then we can create our own tomorrow as a tomorrow in which robots are pleasant things and revolutionise our world.